POPLHLTH 706 : Statistics in Health Science

Medical and Health Sciences

2024 Semester One (1243) (15 POINTS)

Course Prescription

Provides an overview of statistics and statistical methods for health scientists. Covers a range of methods and tests, including regression.

Course Overview

The course covers aspects of essential statistics in health sciences. Students will learn how to interpret medical and epidemiological data and understand the statistics that are commonly reported in medical research reports. Practical skills in the use of the statistical analysis package R will be developed, including basic data management and cleaning, descriptive analysis and the use of common statistical tests and regression models.

Course Requirements

No pre-requisites or restrictions

Course Contacts

Course director: Dr Alana Cavadino, Section of Epidemiology and Biostatistics, School of Population Health.
Email: a.cavadino@auckland.ac.nz 

Course co-lecturer: Dr Arier Lee, Section of Epidemiology and Biostatistics, School of Population Health.
Email: arier.lee@auckland.ac.nz

Course administrator: Upendra Wickramarachchi, School of Population Health
Tel: (09) 923 3058, Email: u.wicks@auckland.ac.nz

Capabilities Developed in this Course

Capability 3: Knowledge and Practice
Capability 4: Critical Thinking
Capability 5: Solution Seeking
Capability 6: Communication
Capability 7: Collaboration
Capability 8: Ethics and Professionalism
Graduate Profile: Master of Public Health

Learning Outcomes

By the end of this course, students will be able to:
  1. Develop an understanding of the rationale and requirement for, and the importance of statistical methods in health data analysis. (Capability 3 and 4)
  2. Recognise and interpret statistical methods and results that are commonly reported in health research. (Capability 3 and 4)
  3. Identify and apply appropriate statistical methods to summarise and analyse health data. (Capability 3, 4 and 5)
  4. Effectively communicate health data description and analysis results. (Capability 3, 4, 5, 6, 7 and 8)
  5. Use R statistical software to manage, summarise and analyse data. (Capability 4, 5 and 6)


Assessment Type Percentage Classification
Coursework 1 - Data description using R 15% Individual Coursework
Coursework 2 - Basic statistical tests using R 15% Individual Coursework
Coursework 3 - Regression modelling using R 20% Individual Coursework
Final Exam 50% Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4 5
Coursework 1 - Data description using R
Coursework 2 - Basic statistical tests using R
Coursework 3 - Regression modelling using R
Final Exam
Note: The semester one examination period is Thursday 6 – Monday 24 June 2024. Examinations are scheduled Monday to Saturday. The examination timetables will not be finalised and available to students until 6-8 weeks into the semester. 

Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect  24 hours of lectures, 8 hours tutorial, 10 hours of reading and thinking about the content and 10 hours of work on assignments and/or test preparation.

Delivery Mode

Campus Experience

Attendance is expected at scheduled activities including tutorials.

The course will not include live online events. Lectures will be available as recordings. Other learning activities including tutorials will not be available as recordings. 

The activities for the course are scheduled as block delivery. Block teaching will take place between 9am and 2pm on 8 Wednesdays across semester 1. Each teaching day includes lectures from 9-12  followed by a tutorial in a computer lab from 1-2pm. Teaching dates are:  28 February, 13 March, 27 March, 17 April, 01 May,  08 May,  22 May, 29 May.

Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

Copies of all PowerPoint slides used in the lectures will be provided, as well as supplementary notes and other learning material. Lectures will also be available as recordings. These will be sufficient for the course and there is no set textbook.

However, students may find benefit in other sources. There are many reference texts and resources for further reading covering concepts taught in this course as well as more in-depth material on related topics that are beyond the scope of this course. Some examples include:
  • Primer of biostatistics. Textbook by Stanton A Glantz.
  • Medical statistics from scratch: an introduction for health professionals . Textbook by David Bowers. Full text available online via the UoA library catalogue.
  • The Epidemiologist R Handbook, 2021. Online resource by Batra, Neale, et al: https://epirhandbook.com/
  • Quick-R. Online resource: https://www.statmethods.net/. Linked to: R in Action - Data analysis and graphics with R. Textbook by Robert I. Kabacoff that significantly expands upon the material in the Quick-R website. 
  • R for Data Science. Textbook by Hadley Wickham and Garrett Grolemund. Available online: https://r4ds.had.co.nz/index.html
  • R graphics. Textbook by Paul Murrell. Full text available online via the UoA library catalogue.
  • The R Book. Textbook by Michael J. Crawley. Full text available online via the UoA library catalogue.

Student Feedback

At the end of every semester students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback and respond with summaries and actions.

Your feedback helps teachers to improve the course and its delivery for future students.

Class Representatives in each class can take feedback to the department and faculty staff-student consultative committees.

Course materials have been revised for 2024 in response to student feedback.

Other Information

This course will use the R statistical package. There will be introductory materials available before the start of the semester, which will benefit students with no experience using this (or similar) software.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed for potential plagiarism or other forms of academic misconduct, using computerised detection mechanisms.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website http://disability.auckland.ac.nz

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption, we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html.


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course students may be asked to submit coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.